Development of Raman Micro-Spectroscopy to Characterize Human Ovarian Cancer Cells
Bibliographic record
Abstract
Raman spectroscopy is a vibrational spectroscopy technique and has demonstrated highly desirable unique analytical capabilities throughout the field of biomedical optics.It has the ability to obtain sensitive measurements of molecular composition, structure and dynamics from very small sample volumes in a non-destructive, non-invasive and label free manner.This makes it useful in the study of the cells as well as tissues.In this work a Raman micro-spectrometer system was developed and applied in vitro to discriminate between the ovarian carcinoma cell lines A2780s (parental wild type) and A2780cp (cisplatin cross radio-resistant variant).These two cell lines represent a good model of tumor tissues of similar origin but with different intrinsic chemo-and radio-sensitivities.Moreover, their radiobiological behavior has been extensively studied and their survival curves under different irradiation schemes are known.The Raman spectra collected from individual cells undergo initial preprocessing (background subtraction, normalization and noise reduction) to yield true Raman spectra representative of the cells.These spectra are analyzed with Principal Component Analysis (PCA) followed by Linear Discriminant Analysis (LDA) to yield a strong separation between the cell lines.The objective of this work was to characterize the spectral differences between the two cell types in order to determine the underlying biochemical basis for this separation.The multivariate classification model constructed using such Raman spectra of ovarian cancer cells could potentially be utilized for early prediction of tumor response.This project provided many challenges, and I was fortunate to receive support from many great people along the way.First and foremost, I would like to thank my supervisor, Dr. Sangeeta Murugkar for her patience and guidance throughout this journey.It's been an absolute privilege working with her.Many thanks also to Carleton Biophotonics Research Group (CBRG) Abrar Ahmad and Dean Sheperdson for helping me with construction of our system, and without them I strongly doubt we would have been capable of getting it running in such short time.I would like to thank my dearest friend Nima Sherafati for helping me whenever I needed him.His support has been a guiding light for me.I would also like to thank our new members, Christopher Dedek, Harry Allen, and Achint Kumar,
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".